Interpolation

In the field of machine learning (ML), interpolation is the process of estimating the value of a function or dataset at points between known data points. Interpolation is often used to fill missing values in a dataset or to remove noise or irregularities in the data.

There are a number of different methods that can be used for machine learning interpolation, including linear interpolation, polynomial interpolation, and spline interpolation. The choice of interpolation method depends on the characteristics of the data and the goals of the analysis.

The simple process of linear interpolation involves fitting a straight line between two known data points and using that line to calculate the function value in between. Although it is fast and simple to use, this method may not be suitable for data with more complex patterns.

Fitting a polynomial function to the data points during polynomial interpolation can be more flexible and appropriate for data with complex patterns. Spline interpolation involves fitting a smooth curve to the data points when the data exhibit smooth, continuous trends.

In machine learning, interpolation can be used to fill in missing values in a dataset, which is very useful when dealing with incomplete or noisy data. It can also be used to remove irregularities in the data, which helps improve the accuracy and robustness of machine learning models.

The role of interpolation in computer vision

In machine learning, interpolation can be used to fill in missing values in a dataset, which is very useful when dealing with incomplete or noisy data. It can also be used to remove irregularities in the data, which helps improve the accuracy and robustness of machine learning models.

References

【1】https://encord.com/glossary/interpolation-definition/